Fully non-invasive blood sugar level monitoring apparatus integrated with real-time health support system

ABSTRACT

A non-invasive continuous blood sugar level monitoring apparatus integrated with a real-time health support system. The blood sugar levels and other vital physiological information of the user can also be tracked wirelessly through the apparatus. The apparatus has an integrated real-time alert and reminder feature for notifying the user during medication and unusual physiological conditions. An automated diet and lifestyle recommendation solution is integrated into the device to help the user maintain healthy blood sugar and blood pressure levels. The low-powered the telemetry device is used for communicating the stored physiological information of the user and the computed results between the network of devices.

TECHNICAL FIELD

The present invention relates to an intelligent telemetry instrumentation for non-invasively monitoring continuous blood sugar levels, blood pressure data, psychological stress and other physiological parameters. It is, in particular, related to clinical monitors, health management gadgets and wearable medical devices involving transmittive optical sensing design.

BACKGROUND OF THE INVENTION

Modern lifestyle and food habits have huge impact on our physiological and phycological health. In this fast-paced society, it has become a necessity to track and manage our health and activities. In fact, globally 1 in 11 of us are suffering from diabetic condition and 21% of the people in US are standing on the diabetic border. In the coming quarter century, this count is expected to raise by more than 200 million and the prevalence is expected to raise by over 25%. The current technology either offers painful periodic invasive monitoring solution or disposable microneedles based monitoring solution. The current continuous glucose tracking devices also fails to monitor blood sugar levels in prediabetic range and does not act as a management or prevention solution. Attempts have been made in the past by inventors and academic scholars to create a non-invasive technology, but their hardware and processing architecture proposals ignores underlying scientific principles of transmittive sensing and real-time processing techniques.

Hence, the invention is directed towards a transmittive apparatus design and real-time processing system that can intelligently overcome the barriers utilizing boundary angle conditions and other signal altering factors like dispersion effects. The disclosure describes an advanced portable continuous blood sugar monitoring solution for tracking blood glucose of both prediabetic and diabetic spectrum, which also has a real-time diet recommendation and lifestyle management system as an integrated prevention solution. The device also includes other health guidance components like blood pressure fluctuation tracking and hypertension management system, and emergency life-support system, etc.

What is needed is:

-   -   1. Transmittive sensing based accurate continuous blood sugar         monitoring apparatus that can work for every segment of the         population;     -   2. An intelligent blood sugar management solution that can act         as an effective medical and well-being guidance system; and     -   3. An integrated general wellness solution for managing other         physiological conditions such as hypertension and emotional         stress.

SUMMARY OF THE INVENTION

The object of the invention is to present a totally non-invasive continuous blood glucose monitoring solution integrated with intelligent medical condition management and automated recommendation system. The apparatus can also be utilized to monitor and manage blood pressure and other health parameters.

First Aspect

In the first aspect, a transmittive configuration based near-infrared optical spectrometer is presented. The near-infrared (Near-IR) spectrometer comprises of a set Near-IR light sources arranged in transmittive configuration with a quantum distance of wavelength number (kλ) between them. An optical lens is placed after the near-infrared at signal probe end, which focuses and constructively interferes near-infrared radiation on the sensing spot. The transmitted optical response is captured and focused by the photodetector-end optical lens on the near-infrared photodetector. The transmitted response is recorded by the near-infrared photodetector. During stationary real-time sensing set-up, the spectrometer may be alternatively arranged in inverted configuration to reduce background noise.

Second Aspect

The second aspect of the invention presents a green optical spectrometer. The green optical spectrometer comprises of two green LED signal probes, of which one LED signal probe is placed at normal direction and the other LED is tilted at a critical angle (0 c). Each LED signal probe has an optical lens placed after them, which focuses and transmits the green signal on the sensing spot in their corresponding direction. An optical lens and green photodetector set are placed in reflective configuration next to the tilted green LED to measure the response reflected by the coarse skin surface. An optical lens and green photodetector set is placed in the transmittive configuration with respect to the normal green LED signal probe to record the transmitted response. During stationary real-time sensing set-up, the spectrometer may be alternatively arranged in inverted configuration to minimize background noise.

Third Aspect

Red light based transmittive dispersion analyser is provided in the third aspect of the invention. The dispersion analyser comprises of a signal probe set of normal red LED and two adjacent red LEDs tilted at an angle and focused on the central photodetector system. The adjacent red LED signal probes are tilted in opposite direction and placed on the either side of the normal LED. A set of optical lenses are placed before the LED signal probes to concentrate and focus the light on the sensing spot. The dispersion analyser further comprises of a set of red photodetectors and corresponding optical lens placed at different response receiving positions. The signal difference between the response of central photodetector and response of other photodetectors are taken to analyse the dispersion effect. During stationary real-time sensing set-up, the spectrometer may be alternatively arranged in inverted configuration to cut-down the background noise.

Fourth Aspect

In the fourth aspect, infrared (IR) radiation dispersion analyser is provided. The dispersion analyser comprises of a signal probe set of normal IR LED and two adjacent IR LEDs tilted at an angle and focused on the central photodetector system. The adjacent IR LED signal probes are tilted in opposite direction and placed on the either side of the normal LED. A set of optical lenses are placed before the LED signal probes to concentrate and focus the light on the sensing spot. The dispersion analyser further comprises of a set of IR photodetectors and optical lens placed at different response receiving positions. The signal difference between the response of central photodetector and response of other photodetectors are taken to analyse the dispersion effect. During stationary real-time sensing set-up, the spectrometer may be alternatively arranged in inverted configuration to curtail background noise.

Fifth Aspect

The fifth aspect of the invention puts forth a low-powered hardware to implement the optical spectrometers.

The hardware comprises of optical signal probes of Near-IR light sources, IR LEDs, Red LEDs and Green LEDs, and photodetector probes of Near-IR photodetector, IR photodetectors, Red photodetectors and Green photodetectors with their respective optical elements. The LED signal probes, photodetector probes and their respective optical elements are arranged according to the spectrometer configurations. The input to the Near-IR LED signal probes are coherently driven through a tuneable BJT/FET based active current amplifier circuit and the set of resistors. A set of three micro-switches are used to alternatively drive the input to the red LEDs and infrared LEDs. The set of three switches are utilized before the red LED and IR LED circuit line to reduce the tracing efforts and component use. A set of resistors are used in the red LED and IR LED circuit line to distribute the input signal. A main micro-switch set is used as a low powered means to shift the input from the LED frontend to the green LEDs, red LED and IR LED circuit line and to the active current amplifier attached to the Near-IR LDs. The LED frontend comprises of LED driver, LED controller, PWM and clock controller, which tunes and sends the input signal through the main micro-switch set.

A BJT/FET based Darlington pair and small signal current source is attached to the Near-IR photodetector, which is used to amplify the low powered Near-IR response. The low power green response signals pass through the optical elements and the green photodetector probes, which are amplified by the Darlington pair and small current source attached to the respective photodetector. The small current sources attached to the photodetectors adds baseline to the response signals. The red response and IR response, recorded by the red photodetector-optical lens system and IR photodetector-optical lens system, are extracted alternatively by using a switch set. The central red-IR photodetector response is separately processed using a stabilizing buffer and circuit line of ADC, Ambient Noise cancellation IC and DAC. The responses of non-central photodetector are summed using an op-amp. The summed non-central signal response passes through the stabilizing buffer unit and circuit line of ADC, Ambient Noise cancellation IC and DAC. The red-IR response signals are extracted through two different circuit lines. One response line is utilized to analyse the total IR-red response and the other circuit line is utilized to extract the dispersion signal. An Instrumental Amplifier is attached to the response lines of summed non-central photodetector output and central photodetector output for extracting the dispersion information. The power line noise in the analysed dispersion signal is filtered through a power notch. The filtered dispersion signal is sent to the microprocessor through an ADC. The central photodetector signal and summed non-central photodetector signal are furthered aggregated using resistors and Transimpedance (TIA) amplifier of the photodetector frontend. The response signals of near-IR light, tilted green light, transmitted green light and red-IR light are processed and filtered using the photodetector frontend circuit elements of TIA amplifier, power notch, ADC and Noise cancellation IC. The processed output response of the individual spectrum is then sent to the microprocessor. A main micro-switch set is attached between the photodetector frontend and response line of different light spectrum, which shifts the output response to respective photodetector circuit based on the input signals. The photodetector-end switch set reduces the overall component use and power consumption. An additional switch set can be utilized to reduce the count of the power notch and Noise cancellation IC.

A non-contact MEMs/NEMs temperature biosensor, attached to the microprocessor, logs the body temperature response and thermal feedback. An ambient temperature sensor, attached to the microprocessor, records the environment temperature and temperature of the internal electronics system. The 9/6 axis MEMs/NEMs accelerometer of the hardware is utilized as a real-time feedback to remove motion noise from the bio-signal response. A set of wireless antennae of WLAN, BLE, GSM and GPS are either externally attached to the microprocessor or integrated inside the microprocessor. The set of wireless antennae communicates the data between the telemetry apparatus, and the set of external storage and computational devices like accessorial mobile devices, server, etc. The set of wireless antennae along with the accelerometer is used for tracking the real-time location and movement signals like phase, speed, steps taken, etc. The microprocessor is used for communicating commands and feedbacks with the internal electronic components of LED frontend, photodetector frontend, accelerometer, temperature biosensors, ambient temperature sensors, other sensors, wireless antennas, USB module, buttons, potentiometer integrated navigator, fancy LED, touch display and other electronics modules. The function of microprocessors also includes computing and storing the required information. A mini-touch display is attached to the hardware for viewing and accessing the real-time medical information, health data and on-device applications. The touch display is also used to calibrate and operate the instrumentation. The fancy LED flashes for displaying different device modes, device status and decorative applications. The buttons and potentiometer integrated navigator are used for operating and calibrating the device. The memory module attached to the microprocessor is utilized for internally storing the information.

Apart from the display unit, the hardware of the telemetry device is internally or externally attached to an additional user interaction system of consisting of mic and speaker. The set of user interaction hardware components is utilized by the user for interacting with the medical and health practitioners for clinical and health analysis. The professionals can send and receive the information, as well supervise the user through the user interaction system. The user interaction unit is also used as the means to perceive the recorded and computed information, and to operate the device and its in-built applications.

The hardware of the telemetry apparatus is attached to a power supply unit, which comprises of power management IC, supercapacitor-battery set, supercapacitor-renewable energy harvester set, wireless coil, USB module and negative voltage converter. The power management IC of the power supply unit, attached to the hardware and microprocessor, regulates the current flow and power supply. The negative voltage converter attached to the power management unit generates the negative reference signal. The USB module and supercapacitor-battery are utilized for powering the electronic circuit. The USB module is also used for communicating the data with the external devices and charging the battery of the internal circuit. The device is wirelessly recharged through the coil. The power supply unit includes an alternative and supplementary power supply unit containing renewable energy harvester and supercapacitor.

Sixth Aspect

The sixth aspect of the invention provides the method for device initialization and apparatus calibration. During the initial device start-up, the age, weight, gene info, BMI, Fat % and contact layer picture of the user is recorded. The recorded contact layer skin colour is processed on a scale of 1 to 10 and the contact picture is again recorded multiple times. The median values of the processed contact layer color are stored and utilized. On unavailability of the contact layer recording, the realistic profile picture of the user is processed, and the values are altered by an adjusting parameter to extract the realistic value of the contact layer skin color. Different blood sugar values, blood pressure values and other health parameters are recorded and processed for calibrating the device. The blood sugar values, blood pressure values and other vital health parameters are recorded during sitting position, standing position, relaxing position, fasting glucose, post-dinner, post-breakfast, post-lunch, post-morning sleep, post-exercise, before dinner, before breakfast, before lunch, before bed-time and also during the hypoglycaemic and hyperglycaemic conditions. If increasing value of hyperglycaemic and hypoglycaemic conditions are recognized, the device is re-calibrated. For diagnosed hyperglycaemic and hypoglycaemic conditions, the apparatus records and stores the blood sugar values, blood pressure values and vital information multiple times a day. If enough calibration values are available, the calibration process is skipped and if lesser number are values are available, then more calibration values are recorded. The Near-IR light sources, green LEDs, IR LEDs and red LEDs are initiated, and the values of the responses are recorded in their respective matrices. The recorded responses are normalized according to the light source area and power. The transmittive green response (GN) and reflective green response (GT) signals are analyzed for DC losses. The recorded red and IR response signals are analyzed to extract the Integrated signal response (Rtot-IRtot), Differential/Dispersion signal responses (Rdiff-IRdiff) and power response (RP-IRP). The body temperature (Btemp) and Ambient temperature response (Atemp) are recorded and the response signals are adjusted as per the temperature stats. Accelerometer is initiated to record movement data and to remove motion noise in the real-time signal. Wireless antennae are initiated and analysed for location and movement data.

Seventh Aspect

The seventh aspect of the invention provides the real-time system for monitoring continuous blood sugar levels. The recorded sensor signals are processed and correlated for extracting the real-time values. The real-time transmittive green sensor values and reflective green sensor values are analyzed using Fast-Fourier Series for DC losses of GTDC and GNDC due to coarse skin layer and normal skin layer. Fast Fourier analysis is applied to GT(G−GT)/GN(G−GN) to detect the signal loss parameter (GPAR). For coarser skin, the green parameter is extracted in terms of tilted green sensor (GTDC) and transmittive green sensor (GNDC) i.e. (GNDC+GTDC)/2; else the processed value of transmittive green sensor (GNDC) is directly used. The Near-IR response signals are adjusted for body temperature values and ambient environment temperature values using statistical methods, and the different resonant values of the Near-IR signals are computed. The mean of different adjusted Near-IR values are computed using NIRT=(NIRA+NIRB+NIRC . . . +NIRN)/N. Fast-Fourier series is applied to Rtot1 to derive oscillatory signal (Rosc). The oscillatory signal (Rosc) is adjusted for blood line dc losses, and then it (Rosc) is adjusted for skin losses using the derived Green signal parameter (GPAR). The oscillatory signal power (Rosc) is compensated from the Near-IR signal (|NIRT1|_(t)=|NIRT|_(t)−X1.|Rosc|_(t)). Then, NIRT1 is adjusted from the IRtot and IRdis for Near-IR dispersion due to other blood particles (NIRT2=NIRT1−X1IRdis−X2.IRtot). Then Near-IR value is adjusted for red differential/dispersion signal (NIR3=NIR2+X3.Rdif). Then, the green parameter is adjusted from Near-IR signal in variable constant form and variable coefficient dependent form (NIR4=NIR3−X4.ln(GPAR−X5)). Linear and non-linear correlation is applied to different processed Near-IR values (NIR4), Color indices (C) and different recorded real-time Blood Sugar Value time values for calibrating the sensors. Then, real time value of continuous blood sugar (BSL) is computed from the calibrated sensor. The sensor is re-calibrated for recognized hyperglycemic and hypoglycemic conditions. The IR sensor, red sensor, current values and calibrated values are analyzed and learnt for tracking BSL, hypoglycemic and hyperglycemic conditions. The real-time values of the continuous blood sugar and blood sugar fluctuation data are stored and displayed. On recognizing the chronic and abnormal blood sugar conditions, the system automatically alerts the life-support network of the user.

Eighth Aspect

In the eighth aspect of the invention, a method to precisely calibrate the blood sugar monitoring is provided. Initially the computed blood sugar values are evaluated for boundary values and threshold fluctuation values. Different blood glucose states of fasting glucose, pre-meal values and post-meal values are evaluated against the boundary fluctuations, threshold values and different blood sugar ranges, and the device is recalibrated accordingly. Based on the detected blood sugar condition of pre-diabetes, hyperglycemia and hypoglycemia, an automated therapy and diet recommendation is presented to the user. The dispersion values are further analyzed and learnt to evaluate the response result.

Ninth Aspect

A method to extract the blood pressure and stress levels are provided in the ninth aspect of the invention. Initially, the red signals are compensated for skin losses using parametric analysis between the green response and red response signals. Fast Fourier analysis is applied to the total and oscillatory signals of the red sensor to extract the power values of the red signal. The analyzed and noise-free red sensor values are further analyzed using non-linear or linear analysis for deducing real-time blood pressure values. The real-time blood pressure values and fluctuations are analyzed to evaluate the blood pressure conditions of stage 1 hypertension, stage 2 hypertension, pre-hypertension and low blood pressure conditions. Based on the recognized blood pressure condition, the user is presented with physician consultation message, diet and health management techniques. The blood pressure values and the temporal fluctuations of the red signal are analyzed through different methods and parameters of user location, user state and user postures. The analyzed blood pressure values and the temporal fluctuations are utilized to evaluate the psychological stress levels of the user. During the state of psychological stress, the user is automatically presented with stress management methods. On recognizing severe blood pressure condition and state of psychological stress, the system automatically alerts the life-support network of the user.

Tenth Aspect

An automated sleep tracking system is presented in the tenth aspect of the invention. The accelerometer signals, body temperature, blood pressure data and blood sugar values are initially evaluated for state of sleep. The variations in blood pressure and blood sugar values are compared against the wake levels, and then derived HP1, HP2 and HP3 parameters are furthered analyzed for recognizing NREM sleep cycle and REM sleep cycle. The computed sleep cycle and time period of the respective sleep cycles are incremented and stored. The actimeter signals are evaluated to verify if the user's sleep is disrupted. On recognizing the state of disturbed sleep, the health and life-style recommendations are provided to the user. Further learning is applied to the signals to simplify the sleep recognition process.

Eleventh Aspect

The eleventh aspect provides a method and software device to calibrate the device. The user data of profile picture, age, BMI, fat %, gene info, weight and height are recorded through the telemetry apparatus or the accessorial mobile apparatus. A picture of the contact surface is recorded and processed through the aforementioned method. The blood sugar and blood pressure calibration values are recorded for different instances of fasting glucose values, before bedtime, before lunch, before dinner, after breakfast, after sleep, after dinner, after lunch, and after exercise. The user can also record information on the micro-nutrition and macro nutrition, and meal-information through the telemetry apparatus or the accessorial mobile apparatus. The real-time information and data trends on blood sugar levels, blood pressure levels, neural activity, pulse rate, oxygen saturation and body temperature are automatically displayed on the device along with health sense message. The device comprises of automated real-time reminder and alert system to notify the user during the instances of medication and chronic medical conditions. The device further comprises of recommendation system, which guides the user with health practices and diet management techniques for the diagnosed health condition.

Twelfth Aspect

The twelfth aspect shows an optimization method for estimating the health and calibration parameters from the previously recorded data of the user database. The color index, age, BMI, fat %, gene Info, sensor intensity, signal response and real-time calibration values of the user are matched with the previously recorded parameters in the database. The optimization parameters of color index, sensor calibration data, healthy H.R. index, performance index and progress index are learnt and derived from the central database. The optimization parameters are returned to the user device, which is used in processing the real-time biological information and other health parameters.

Thirteenth Aspect

In the thirteenth aspect, a parallel computational network is provided. The parallel computational network enables the computation with much higher speed and efficiency, while keeping the complexity low. The network of parallel computation network comprises of internal microprocessor, external server computers, accessorial mobiles devices, external computers and other synchronized devices. The external servers are used for executing computational process, and as well as for remotely storing the information. The accessorial mobile devices and other synchronized devices are also used to compute and store the information. The network of parallel computing devices are accessed through wireless methods of ‘WLAN, BLE, GSM’ and through other possible modes of communication. Whenever necessary, stored information and computed results are communicated between the telemetry apparatus and network of devices.

Fourteenth Aspect

An emergency response system is presented in the fourteenth aspect. On recognizing emergency trigger, the system validates the status of the wireless antennae and switches it on. The location data are recorded through the wireless antennae set, and the biometric and other vital information are recorded through the internal bio-sensors. The recorded information is transmitted to the central server, SOS network, support network and to the near-by mobile devices through the wireless methods. The devices are synchronized, and the next set data are transmitted. The wireless data transfer occurs directly or via medium of central server.

Fifteenth Aspect

The fifteenth aspect of the invention provides a single clipper based ring embodiment form of the telemetry apparatus and the optical spectrometer. The ring comprises of a main body frame and an extending clipper element. The signal probe set of Near-IR spectrum, Infrared spectrum, red spectrum, tilted green spectrum and transmittive green spectrum, and the set of Near-IR photodetector, Infrared photodetector, red photodetector and green photodetectors are packaged on the inner contact surface. The photodetectors are placed in the alignment with their corresponding signal probes for recording the optical response. A non-contact temperature sensor is placed on the inner contact surface, which is utilized to record the temperature response of the body. The inner surface of the ring comprises of a sponge base, which is used to reduce the contact vibrations during the real-time measurements. A micro-USB port is embedded on the side surface of the ring apparatus, which is utilized for charging the device and for data transfer purposes. A fancy LED is embedded inside the ring's frame, which is used for indicating different device operating modes and also for representing decorative applications. The device comprises of navigator crown and buttons, which are utilized to operate the telemetry apparatus. A coil is placed inside the device, which is utilized as a wireless means to power the device and charge the device battery. The speaker and mic are embedded on the device, which are used to attend the phone-calls, perceive different responses and operate the apparatus. The clipper element of the device comprises two clips and two hinges, which are used as a size-adjustable feature to fasten the device. The overall device is covered with a water-proof coating.

Sixteenth Aspect

In the sixteenth aspect, a dual clipper based ring embodiment form is provided. The ring comprises of optical spectrometers of green optical spectrometer, Near-IR spectrometer, red and IR spectrometer. A disposable foam base is placed near the sensors to reduce the movement errors in the real-time recording. The device comprises of button set and navigator, which are used to operate the device. The device is fastened to the user with the help of the holding clips. The holding clips are affixed on the upper and lower side of the ring through clip hinges. The holding clips has extender hinges in the middle of the holding clips, which are used to adjust the grip size.

Seventeenth Aspect

The seventeenth aspect of the invention presents a solar module powered portable telemetry monitoring embodiment form. A transmittive sensing spectrometer with foam base is embedded on the finger placement area of the apparatus. A set of buttons are embedded on the side surface of the device, which are used to operate the device. A USB port is attached on the side surface of the device, which is used to transfer data and to power the device battery. The device comprises of touch-screen, which is used as the means to access the information and operate the telemetry apparatus. The back surface of the device is attached to a solar module. The solar module has an actuatable module 1 and actuatable module 2, which are attached to each through an actuatable hinge. The actuator hinge along with an actuator automatically extends the solar module for absorbing more solar energy. The solar module is used as an auxiliary renewable powering unit. The device further comprises a wearable chord with molded extender clip and extender chord, which is used as a method to modify the size of the chord. The device is further coated with water proof coating.

Eighteenth Aspect

In the eighteenth aspect of the invention, an earphone based embodiment form is presented. The device has transmittive sensing spectrometer near earlobe attachment area. A fancy LED is embedded in the ear hook of the device, which emits light to represent different operating modes and device status. The music ear-buds are attached to the rear-end of the device. The ear-bud and ear-hook are used to fasten the device to the user.

Nineteenth Aspect

The nineteenth aspect of the invention presents a fancy LED apparatus. The fancy LED device comprises of multi-colored LED encased in a line of multiple optical tubes. The light emitted by the fancy LED is observed inside the multiple optical tubes.

BRIEF DESCRIPTION OF THE ARTWORK

FIG. 1 is the transmittive near-infrared optical spectrometer apparatus;

FIG. 2 is the green optical spectrometer apparatus;

FIG. 3 is the transmittive red optical spectrometer apparatus;

FIG. 4 is the transmittive infrared optical spectrometer apparatus;

FIG. 5A shows the low powered electronic hardware design of the telemetry at the signal probe end;

FIG. 5B shows the low powered electronic hardware design of the telemetry at the photodetector end;

Series of FIG. 6 show the method to calibrate the telemetry apparatus;

Series of FIG. 7 show the processing method for initialization and normalization;

Series of FIG. 8 is the real-time system for monitoring the continuous blood sugar levels;

Series of FIG. 9 is the method of blood sugar analysis for recognizing the hypoglycaemia, hyperglycaemia and unusual blood sugar fluctuations;

FIG. 10 is the real-time system for monitoring continuous blood pressure levels;

Series of FIG. 11 is the method of blood pressure analysis for recognizing the hypertension, hypotension, and unusual blood pressure fluctuations;

FIG. 12 is the automated real-time system for monitoring emotional stress levels;

FIG. 13 is the real-time and automated sleep tracking system;

FIG. 14 shows a program for operating the telemetry apparatus using the buttons and navigator;

FIG. 15 is a learning method for estimating processing parameters from the user database;

FIG. 16 is the design of automated emergency response system;

FIG. 17 shows the network of devices based parallel computational and storage method;

FIG. 18 is the design of fancy LED apparatus;

Series of FIG. 19 show automated interface for recording user information and calibration values;

FIG. 20 is the automated interface for recording and accessing detailed diet information;

Series of FIG. 21 show the automated interface for accessing real-time biological information;

Series of FIG. 22 show the interface of the automated real-time alerting system;

Series of FIG. 23 show the real-time medication reminders;

Series of FIG. 24 show the sample interface of the automated recommendation system;

FIG. 25A and FIG. 25B show a single clipper based smart ring embodiment form of the telemetry apparatus;

FIG. 26 shows a dual clipper smart ring embodiment form of the telemetry apparatus;

FIG. 27A, FIG. 27B and FIG. 27C show the solar powered handheld monitoring embodiment form of the telemetry apparatus; and

FIG. 28 is the ear attachment embodiment form of the telemetry apparatus.

DETAILED DESCRIPTION OF THE INVENTION

The principle of the described invention is not intended to limit to the specific device or instrumentation application. The disclosure can be chiefly classified into live clinical diagnostic instrument, telemetry medical apparatus, mobile wellness management device, automated recommendation system, real-time intelligent medical reminder, software medical device and other forms of health management devices.

FIG. 1 is the design of infrared spectrometer apparatus for transmittive sensing. The set Near-infrared light sources of 2-3-4 are embedded on the signal source board 1 at a quantum distance of kλ between the light sources. The light emitted by the 2-3-4 are constructively focused by a near-infrared optical lens system 5 on the sensing spot. The quantum distance of wavelength number is maintained between the LDs and the light source objects to obtain a constructive interference (i.e. the distance can vary depending on the relative angle between the light sources or any coherent sources such as slits, etc). The transmitted optical response is focused by the near-infrared optical lens system 6 on the near-infrared photodetector 7 placed on the photodetector board 8. The set of lens system of 5-6 are used to facilitate the transmission of near-infrared through the sensing spot, which would be otherwise lost due to lossy nature of the near-infrared radiations.

FIG. 2 shows the green optical spectrometer apparatus. The apparatus comprises of both the transmittive and reflective arrangement to precisely extract the green response and the relevant physiological parameters. The transmittive green spectrometer apparatus comprises of green LED signal probe 9 embedded on the signal probe board 10. The light emitted by the green light source 9 is captured and focused by the signal probe end green optical lens system 11. The transmitted green optical response is concentrated and focused by the optical lens system 12 on the green photodetector 13 placed on the green photodetector board 14. The reflective green spectrometer comprises set of green LED 15 and green optical lens system 16 at signal probe end, which are tilted at θ_(c) angles. The input light signal injected by the green LED 15 and the optical lens system 16 at the critical angle bounces from the skin boundary. The reflected response is captured and focused by the optical lens 17 on the green photodetector 18, which are embedded at an optimal noise-free response recording spot.

FIG. 3 shows the red indicator apparatus for transmittive red dispersion analysis. The apparatus comprises of three red LED signal probes 20-21-22 embedded on the LED signal probe board 19 and their corresponding red optical system of 23-24-25 are aligned in different directions. The central red LED signal probe 20 and its red optical lens system 23 are placed in the normal direction with its input focus on the central photodetection spot. The non-central red LED signal probes 21-22 and their corresponding red optical lens system 24-25 are embedded on the adjacent positions to the normal red signal probe system 20-23. The adjacent red LED 21 and its optical lens system 24, embedded on the left-side, are tilted to focus the input signal on the central photodetection spot. The non-central red LED 22 and its optical lens system 25, embedded on the right-side, are tilted to focus the input light on the central photodetection spot. The central photodetector of 26 and non-central photodetectors of 27-28 are embedded on the photodetector board 32 at different response recording spots. The central red photodetector 26 and its red optical lens system 29 are placed at the central response receiving position. The non-central red photodetector 27 and its red optical lens system 30 are placed on the adjacent left position to the central red photodetector 26 for recording the dispersion signals. The non-central red photodetector 28 and its red optical lens system 31 are placed on the adjacent right position to the central red photodetector 26 for recording the dispersion signals.

FIG. 4 shows the infrared indicator apparatus for transmittive infrared dispersion analysis. The apparatus comprises of three infrared LED signal probes 34-35-36 embedded on the signal probe board 33 and their corresponding infrared optical system of 37-38-39 are aligned in different directions. The central infrared LED signal probe 34 and its infrared optical lens system 37 are placed in the normal direction with its input focus on the central photodetection spot. The non-central infrared LED signal probes 35-36 and their corresponding infrared optical lens system 38-39 are embedded on the adjacent positions to the normal infrared signal probe system 34-37. The adjacent infrared LED 35 and its optical lens system 38, embedded on the left-side, are tilted to focus the input signal on the central photodetection spot. The non-central infrared LED 36 and its optical lens system 39, embedded on the right-side, are tilted to focus the input light on the central photodetection spot. The central photodetector of 41 and non-central photodetectors of 42-43 are embedded on the photodetector board 40 at different response recording spots. The central infrared photodetector 41 and its infrared optical lens system 44 are placed at the central response receiving position. The non-central infrared photodetector 42 and its infrared optical lens system 45 are placed on the adjacent left position to the central infrared photodetector 41 for recording the dispersion signals. The non-central infrared photodetector 43 and its infrared optical lens system 46 are placed on the adjacent right position to the central infrared photodetector 41 for recording the dispersion signals. Similarly, light sources of different spectrum can be utilized for evaluating the real-time biological information and spectral dispersion data.

FIG. 5A and FIG. 5B show the electronic hardware architecture of the telemetry to implement the spectrometer apparatuses.

FIG. 5A shows the signal probe end hardware design with micro-sensors, wireless antennae, user interaction system and power supply unit. The input to the near-infrared light sources of 56, 57 and 58 are coherently driven through a resistor line and a tuneable FET/BIT based active amplifier circuit 67. A set of switches 68-69-70 are placed between the corresponding red LED-infrared LED set of 61-64, 62-65 and 63-66, which are utilized to alternatively drive input to the red LED signal probes of 61-62-63 and infrared LED signal probes of 64-65-66. The signal input is variably triggered and sent through a LED frontend comprising of LED driver 72, PWM 74, switch set 73, LED controller 75 and clock controller 76. A signal probe end primary switch set 71 is utilized for connecting the LED frontend of 72-73-74-75-76 to the red-infrared signal input line, Green LED 59, tilted Green LED 60, and active amplifier circuit 67 attached to the near-infrared LDs 56-57-58. The primary switch set 71 reduces the overall component use, power consumption and electrical tracing efforts. The optical lens system of 47, 48, 49, 50, 51, 52, 53, 54 and 55 tunes and focuses the input radiation of corresponding light sources of 56-57-58, 59, 60, 61, 62, 63, 64, 65 and 66 on the sensing spot.

A MEMs/NEMs non-contact temperature biosensor 77 is attached to the hardware for extracting the real-time body temperature and temperature feedback. An ambient temperature sensor 78 of the hardware is utilized for extracting real-time environment temperature and feedback of the internal electronics. A MEMs/NEMs 9/6-axis accelerometer 79 is attached to the hardware, which is utilized as real-time motion feedback and as a means to compute movement signals. The wireless antennae set of GPS 80, GSM 83, WLAN 81 and BLE 82 of the hardware are used for communicating the information between the telemetry apparatus and external devices. The wireless antenna set of 80-81-82-83 is also utilized to compute the real-time location and movement data of steps taken, speed, phase, etc. The microprocessor 100 attached to memory 101, is used for communicating with the internal electronics and operating the internal electronic components. The microprocessor 100 with memory 101 is also utilized for computing and storing the required information.

A mini touch display 84 is attached to the hardware, which is utilized for viewing and accessing the real-time medical information, real-time medical alerts, automated recommendations, notifications, data trends, daily health check-up data and other essential information. The touch display 84 is also used for operating the telemetry apparatus and its in-built applications. Apart from the display unit 84, the hardware of the telemetry device is attached to a user interaction system of mic 85, speaker 86, button set B1-B2-B3 87-88-89 and potentiometer integrated navigator 90. The navigator crown 90 comprises of a potentiometer and a fixed impedance component. The set of interaction components of 85-87-88-89-90 are utilized for operating the telemetry apparatus and accessing the in-built applications. The set of user interaction hardware components of 85-86-87-88-89-90 are utilized as a means for interacting with the professional medical and health practitioners for clinical and health analysis. The speaker 86 is also used for perceiving the recorded and computed information. A fancy LED circuit 91 is attached to the hardware, which is utilized for automatically indicating the user condition, displaying decorative applications and representing different operating modes and device status.

The hardware of the telemetry apparatus is powered by a power supply unit comprising of power management IC 92, supercapacitor 93-battery set 94, supercapacitor 95-renewable energy harvester 96, wireless coil 98, USB module 97 and negative voltage converter 99. The power management IC 92 is used to regulate power supply. The supercapacitor 93-battery 94 is utilized for energy storage and powering the internal electronics. The supercapacitor 95-renewable energy harvester 96 is used as the auxiliary powering unit. The wireless coil 98 is used as the wireless method to charge the battery and power the internal electronics. The negative signal reference is generated by the negative voltage converter 99. The USB module 97 is used for powering the electronic circuit, charging the internal battery and communicating the data with the external devices.

FIG. 5B shows the photodetector probe end hardware design of the telemetry apparatus. The output response is focused by optical lens system of 102, 103, 104, 105, 106, 107, 108, 109 and 110 on the corresponding photodetector probes of 111, 112, 113, 114, 115, 116, 117, 118 and 119. The near-infrared output response recorded by the near-infrared photodetector probe 111 is shifted by small signal source 120 and amplified by the darlington pair 121. The transmittive green response recorded by the green photodetector probe of 112 is shifted by small signal source 122 and amplified by the darlington pair 123. The reflected green response recorded by the green photodetector probe of 113 is shifted by small signal source 124 and amplified by the darlington pair 125. A set of switches of 126, 127 and 128 are placed between the corresponding set of red photodetector-infrared photodetector of 114-117, 115-118 and 116-119. The set of switches of 126, 127 and 128 are utilized to alternatively record output response of the red photodetector probes of 114-115-116 and infrared photodetector probes of 117-118-119.

The response of red-infrared photodetectors set are taken through the output lines of central response line 129 and summed non-central response line 130. An op-amp IC 132 is utilized to sum the output response of the non-central photodetectors. The output line of central photodetector and non-central photodetectors are stabilized through a buffer circuit of 131 and 133. The summed non-central photodetector response and central photodetector responses are filtered and processed using a circuit line of ADC 134, ambient noise cancellation IC 135 and DAC 136. An Instrumental amplifier 137 with gain is attached to the output line of non-central photodetector response line and central photodetector line for extracting the real-time dispersion information. The real-time dispersion is further filtered and recorded through a circuit line of power notch 138 and ADC 139. The processed output response lines of the individual light sources are attached to the transimpedance amplifier circuit or op-amp circuit 141 through a photodetector end primary switch set 140. The photodetector end primary switch set 140 is utilized as a means to reduce the component use and overall power consumption. The output response through op-amp circuit 141 is filtered and processed through a circuit line of power notch 142, ADC 143 and ambient noise cancellation IC 144.

FIG. 6A, FIG. 6B and FIG. 6C show the method to calibrate the telemetry device. The user information of age, weight, BMI, fat %, gene info and contact layer's photograph are recorded during the initial device start-up. The recorded contact skin picture is analysed in the RGB hex code and deduced on a scale of 1 to 10. The device prompts the user to re-record the contact picture multiple times and the recorded contact skin picture is analysed to deduce the color of the contact surface. On unavailability of the contact surface picture, the realistic profile picture of the user is recognized, and the profile picture values are altered by an adjusting parameter to extract the contact layer skin color values. The blood sugar levels, blood pressure data and other real-time biological information of the user are recorded during the sitting position, standing position, relaxing position, fasting glucose, post-dinner, post-breakfast, post-lunch, post-morning sleep, post-exercise, pre-dinner, pre-breakfast, pre-lunch, before bed-time, hypoglycaemic state and hyperglycaemic state. The device prompts the user to re-record the blood sugar data, blood pressure levels and other real-time biological information for unusual fluctuations and increasing hyperglycaemic and hypoglycaemic conditions.

FIG. 7A, FIG. 7B and FIG. 7C show the process chart for sensor initialization and sensor response normalization. The near-infrared LDs, tilted green LED, transmittive green LED, red LEDs, infrared LEDs and biosensors are initialized in a cyclic manner. The near-infrared response of different modes are recorded and normalized with respect to the area (NIRA, NIRB, NIRC, NIRD . . . ). The tilted green response and transmittive green response are recorded and normalized with respect to the area (GT and GN). The red response values of red Integrated Power Signal (Rtot), red Differential Signal (Rdiff) and Red Power Value (RP) are recorded and normalized with respect to the area. The values of the red signal response are stored with respect to calibrated values in the Rnn Matrix. The infrared response values of IR Integrated Power Signal (IRtot), IR Differential Signal (IRdiff) and IR Power Value (IRP) are recorded and normalized with respect to the area. The values of the infrared signal response are stored with respect to calibrated values in the IRnn Matrix. The system initializes the bio-temperature sensor and ambient temperature sensor for recording the real-time bio-temperature values (Btemp) and ambient temperature values (Atemp). The accelerometer and wireless antennae are initialized for recording the movement data, location data and establishing wireless communication.

FIG. 8A, FIG. 8B and FIG. 8C show the real-time system for monitoring the continuous blood sugar levels. The recorded green sensor response of GT and GN are analyzed to recognize the green sensor DC parameter losses (GTDC and GNDC). The green parameter (GPAR) is deduced from green sensor DC parameter based on the type of the contact surface (GPAR=((GNDC+GTDC)/2); for coarse transmittive device (or) GPAR=GNDC; for transmittive device). The recorded near-infrared response values are adjusted as per the temperature stats of the bio-temperature and ambient temperature response and the mean of the adjusted near-infrared modes (NIRT) are deduced (NIRT=(NIRA+NIRB+NIRC . . . +NIRN)/N). The real-time system processes the recorded red signal to deduce oscillatory red signal values (Rosc) and the oscillatory red response values (Rosc) are adjusted for skin losses using the green parameter (GPAR). The blood line losses free 1^(st) order near-infrared sensor value (NIRT1) is extracted from the oscillatory red response (Rosc) and normalized near-infrared response (NIRT) using linear and non-linear analysis method (NIRT1|_(t)=|NIRT|_(t)−X0.|Rosc|_(t)). The first order near-infrared (NIRT1) is processed with the infrared response (of IRtot and IRdis) for adjusting the near-infrared response for non-haemoglobin particle and other blood particle losses (NIRT2=NIRT1−X1IRdis−X2.(IRtot+IRP). The 2^(nd) order near-infrared response (NIRT2) response is further linearly or non-linearly correlated with the red differential value (Rdiff) for extracting the 3^(rd) order near-infrared response (NIRT3=NIRT2+X3.Rdif). The extracted 3^(rd) order near-infrared response (NIRT3) is analyzed with green parameter (GPAR) in the equation form of either power exponent or linear representation of unknown intercept and unknown coefficient for extracting the 4^(th) order near-infrared response (NIRT4=NIRT3−X4.ln(GPAR−X5) (or) NIRT4=NIRT3−X4.ln(GPAR)+X5). The processed near-infrared response is adjusted for recorded color index C and real-time blood sugar values using non-linear and linear correlation. The processed near-infrared response is correlated with the recorded blood sugar calibration values utilizing non-linear and linear correlation for computing the real-time continuous blood sugar values. The real-time blood sugar values (BSL) and blood sugar fluctuations (ΔBSL) are stored and displayed. The calibrated real-time values are further learnt with respect to the calibrated values, red response and infrared response for recognizing hypoglycemic and hyperglycemic data and blood sugar levels. The real-time system records additional calibration values for recognized conditions of hypoglycaemia, hyperglycaemia and unusual blood sugar fluctuations.

FIG. 9A and FIG. 9B show the method of blood sugar analysis for recognizing the hypoglycaemia, hyperglycaemia and unusual blood sugar fluctuations. The values of continuous blood sugar values (BSL) and blood sugar fluctuations (ΔBSL) are analyzed during fasting glucose state, post-meal state, post-sleep condition, regular condition, and pre-meal state for recognizing prediabetic threshold condition, hyperglycaemia threshold condition and hypoglycaemia threshold condition. The real-time system analyses BSL fluctuation values (ΔBSL) and real-time BSL values with respect to user location. The pulse rate and blood sugar data are evaluated for threshold blood sugar conditions and other health issues. The real-time system analyses and evaluates the red signal values, red signal dispersion values, infrared radiation dispersion values and visible signal values [(Rtot-R)/Rtot, R/Rtot, Rdiff/Rtot, IRdiff, IRtot, _ _ _ ] for learning and recognizing unusual blood sugar fluctuations, hyperglycaemia, hypoglycaemia and prediabetes conditions. The system informs the user with information on the recognized health condition, present blood sugar levels and current blood sugar fluctuations. Subsequently, the system verifies probable symptoms, and automatically generates and displays recommendations from database on therapy methods, treatment centres, lifestyle practices, diet suggestions, required physical activities, mitigation methods and medication advice to treat and manage the recognized blood sugar conditions. The real-time system also automatically notifies and alerts the life-support network with a warning message and information on user data, user condition, user location, recognized health condition, present blood sugar levels, current blood sugar fluctuations and other essential data.

Based on the real-time data and recognized health conditions, the user is automatically presented with real-time medical alert, medication reminder and information on location of the medication.

FIG. 10 shows the real-time system for monitoring continuous blood pressure levels. Initially, the recorded red signal response is adjusted according to the green signal response (Rtot1=Rtot−Y.ln(GPAR)). Then the oscillatory values of the red signal (RT_(osc)) are derived from the adjusted red signal values. Then, the peak to peak cycle of the red signal is analysed for a fixed time span for deriving the power of the red oscillatory signal (RP=Σ∫₀ ^(tpeak)|RTosc|*|RTosc|). The linear and non-linear correlation is applied to the red signal power and calibrated blood pressure value to compute the real-time blood pressure (Ex: BP=X.RP). Then, the sensors are calibrated for tracking real-time blood pressure. The computed continuous blood pressure values are stored and displayed.

FIG. 11A and FIG. 11B show the method to analyse the continuous blood pressure data for recognizing the hypertension, hypotension, and unusual blood pressure fluctuations. The values of continuous blood pressure values (BP) and blood pressure fluctuations (ABP) are analyzed during fasting glucose state, post-meal state, post-sleep state, post-meditation state, regular condition, and pre-meal state for recognizing hypertension, hypertension stage 2, hypotension, and unusual blood pressure fluctuations. The system further analyses the blood pressure values (BP) and blood pressure fluctuations (ABP) for different locations. The system informs the user with information on the recognized health condition, present blood pressure levels and current blood pressure fluctuations. Subsequently, the system verifies probable symptoms, and automatically generates and displays recommendations from database on therapy methods, treatment centres, lifestyle practices, diet suggestions, physical activities, mitigation methods and medication advice to treat and manage the recognized blood pressure condition. Then, the real-time system also automatically notifies and alerts the life-support network with a warning message and information on user data, user condition, user location, recognized health condition, present blood pressure levels, current blood pressure fluctuations and other essential data. Based on the real-time data and recognized health condition, the user is automatically presented with real-time medical alert, a message to consult the doctor, medication reminder and information on location of the medication.

FIG. 12 shows the real-time system and automated method for recognizing psychological stress. The real-time system initially evaluates the real-time blood pressure and blood pressure fluctuations for verifying the state of psychological stress. Then, neural parameters of HP1, HP2 and HP3 are derived utilizing temporal analysis. The peak to peak temporal values of red signal response are evaluated for 0.05 s interval difference for deriving HP1. The peak to peak root mean square and mean values are evaluated for deriving HP2 and HP3. The derived parameters of HP1, HP2 and HP3 are compared with the resting values of HP1, HP2 and HP3 for recognizing the state of emotional stress. The system verifies the location data for verifying the state of emotional stress with respect to the relevant location (Ex: stress at work and home is common, else it is chronic stress condition). Then, the real-time system automatically notifies the user regarding the state of emotional stress and generates suggestions to manage the stress through exercise, guided meditation and diet and social networking platform. The real-time system also automatically alerts and notifies the life-support network with a warning message and information on user data, user condition, user location, recognized emotional condition.

FIG. 13 shows an automated sleep tracking system. The real-time system evaluates the movement data, body temperature, blood sugar levels, blood pressure data and bio-signal data of the user for recognizing the state of sleep. Then, the system assesses the values of blood pressure data, blood sugar levels and neural parameters (of HP1, HP2 and HP3) with sleep and wake data for recognizing REM sleep cycle and NREM sleep cycle. The REM cycle duration, NREM cycle duration, total sleep duration and sleep health are incremented and cached. The computed results are stored and displayed. The real-time system further analyses the actimeter data and sleep results to automatically recognize the sleep quality and the disturbed sleep condition. Based on the recognized sleep quality, the symptoms are verified, and the system automatically generates recommendations from database on recovery techniques, meditation methods, therapy methods, treatment centres, lifestyle practices, diet suggestions, physical activities, medications and health advice to manage the recognized sleep disorder. The real-time system also automatically alerts and notifies the life-support network with a warning message and information on user data, user condition, user location and recognized health condition. A learning method is applied on the derived parameters for reducing analysis parameters count, mode switching, complexity and power consumption of the processing method.

FIG. 14 shows a program for operating the telemetry apparatus using the buttons and navigator input. The long hold of button B1 turns on/off the device and short hold of button B1 swaps the operating modes of the device. The three short holds of the button B2 switches on/off the IOT parallel computational mode and wireless mode of the device. The long hold of button B2 facilitates the wireless synchronization and wireless data transfer between the telemetry apparatus and wireless devices. On recognizing long hold of the button B3, the device prompts the user to record the calibration values and real-time biometric values. The 5 short holds button B3 marks psychological stress levels of the user. Simultaneous long hold of B1/B3 and B2 silently triggers Emergency Alert in the wireless life-support network. Simultaneous long hold of B1 and B3, triggers alarm and medical emergency alert in the wireless life-support network. The rotation of the navigator crown swaps internal applications of the current mode in the direction of voltage shift or adjusts the intensity of the fancy LED.

FIG. 15 shows a user database based method for estimating calibration and health parameters. The color index, age, BMI, fat %, gene Info, sensor intensity, signal response and real-time calibration values are recorded from the individual user devices and sent to the central server. The values sent from the user device to the central server are analyzed and statically matched with the previously recorded parameters of the database. The optimization parameters of color index, sensor calibration data, healthy H.R. index, performance index and progress index are learnt and derived from the central database. The parameters are returned to user device, which is utilized for processing the real-time biological information and other health parameters.

FIG. 16 shows the design of the automated emergency response system. The emergency response system comprises of near-by synchronized mobile devices, SOS network, paired life-support devices and devices in the location of user's vicinity. On recognizing emergency trigger, the system checks for the status of the wireless antennae and the system automatically turns on the switched off wireless antennae. The location data and real-time biological information are recorded through the wireless antennae set and the internal sensors. The recorded information is transferred to the central server, SOS network, synchronized life-support device and the devices in the vicinity of user location. The set of life-support devices gets synchronized and receives the dataset. The life-support network triggers the primary network for transferring next dataset to the life-support network. The wireless data transfer occurs through directly via medium of central server and through other wireless methods.

FIG. 17 shows the network of wireless computational and storage devices. The Telemetry device 145 transfers the information to the server computer 146 and the other accessorial devices 147 thorugh wireless methods. The accessorial mobile apparatus 147, server computer 146 and other network devices are utilized for parallelly computing and storing the information. The network of devices based method is used as a faster and efficient means to compute and store the required information. When necessary, the user device 145 retrieves the computed and stored information from the server 146 and accessorial devices network 147.

FIG. 18 shows the fancy LED apparatus. The fancy LED 148 emits multi-colored light in the line of branching multiple optical tubes 149. The light emitted to represent different device modes, device status and decorative application is perceived through the different branches of the multiple optical tubes 149.

Series of FIG. 19 show sample user interface of the telemetry apparatus and synchronized accessorial mobile device for recording user information and calibration values. FIG. 19A shows the user interface 150 for recording the essential user information. During the device startup, the profile picture 151, user name 152, age 153, basal metabolic index 154, fat % 155, weight 156, height 157 and gene info 158 of the user are recorded through the real-time telemetry or the accessorial mobile apparatus. FIG. 19B shows the interface 159 for recording contact picture. FIG. 19C is the interface 160 of the telemetry apparatus and the accessorial mobile apparatus for recording the calibration values of real-time blood sugar levels and blood pressure data during the device startup. FIG. 19D is the interface 161 of the telemetry apparatus and the accessorial mobile apparatus for recording the calibration values of real-time blood sugar levels and blood pressure data during the state of fasting glucose. FIG. 19E is the automated interface 162 of the telemetry apparatus and the accessorial mobile apparatus for recording the post morning sleep calibration values of real-time blood sugar levels and blood pressure data. FIG. 19F is the interface 163 of the telemetry apparatus and the accessorial mobile apparatus for recording the post-breakfast calibration values of real-time blood sugar levels and blood pressure data. FIG. 19G is the interface 164 of the telemetry apparatus and the accessorial mobile apparatus for recording the pre-lunch calibration values of real-time blood sugar levels and blood pressure data. FIG. 19H is the interface 165 of the telemetry apparatus and the accessorial mobile apparatus for recording the post-lunch calibration values of real-time blood sugar levels and blood pressure data. FIG. 19I is the interface 166 of the telemetry apparatus and the accessorial mobile apparatus for recording the post-exercise calibration values of real-time blood sugar levels and blood pressure data. FIG. 19J is the interface 167 of the telemetry apparatus and the accessorial mobile apparatus for recording the pre-dinner calibration values of real-time blood sugar levels and blood pressure data. FIG. 19K is the interface 168 of the telemetry apparatus and the accessorial mobile apparatus for recording the post-dinner calibration values of real-time blood sugar levels and blood pressure data. FIG. 19L is the interface 169 of the telemetry apparatus and the accessorial mobile apparatus for recording the before-bedtime calibration values of real-time blood sugar levels and blood pressure data.

FIG. 20 shows automated user interface 170 of the telemetry apparatus and synchronized accessorial mobile device for recording and accessing detailed diet information. The device automatically prompts the user to record detailed diet information of the meal name 171, meal quantity 172 and macronutrition and micronutrition 173.

Series of FIG. 21 show automated user interface of the telemetry apparatus and synchronized accessorial mobile device for accessing detailed real-time biological information. FIG. 21A is the automated interface 174 with real-time information on blood sugar levels, blood pressure data, neural activity, heart rate, oxygen saturation ratio and bio-temperature with health sense message 175. The health sense message 175 shows the current status and progress of the stress management and other health disorder management. FIG. 21B is the sample interface 176, which shows real-time information on current blood sugar levels 177 and past blood sugar trend 178. FIG. 21C is the automated interface 179, which shows real-time information on current blood pressure levels 180 and past blood pressure trend 181. FIG. 21D is the automated interface 182, which shows real-time information on pulse rate 183 and oxygen saturation ratio 184 with real-time signal pattern 185. The automated user interfaces are automatically displayed on the user device in a timely manner.

Series of FIG. 22 show sample interface of the automated real-time alerting system. FIG. 22A is the sample interface 186 that displays an automated warning 187 based on the real-time data with information on the unusual fluctuation (of the blood sugar levels 188). FIG. 22B is the sample interface 189 that displays an automated warning 190 based on the real-time data with information on the unusual fluctuation (of the blood pressure levels 191).

Series of FIG. 23 show the real-time medication reminders that is displayed for unusual real-time biological data fluctuations and unusual physiological state. FIG. 23A is the interface 192 that displays an automated warning 193, unusual fluctuation message 194 and a medication reminder message 196 with information on real-time blood sugar levels 195. FIG. 23B is the interface 197 that displays an automated warning 198, unusual fluctuation message 199 and a medication reminder message 201 with information on real-time blood pressure 200. FIG. 23C is the interface 202 that displays an automated reminder 203, notification on blood sugar abnormality 204 and a medication reminder message 206 with information on real-time blood sugar levels 205. FIG. 23D is the interface 207 that displays an automated reminder 208, notification on blood pressure abnormality 209 and a medication reminder message 211 with information on real-time blood pressure levels 210.

Series of FIG. 24 show the sample interface of the automated recommendation system. The automated recommendations are displayed on daily basis for specific health management purpose and also based on the real-time biological information. FIG. 24A is the sample interface 212 that displays unusual biological information with location 213, health management method 214 for the recognized health condition and real-time physiological data 215. FIG. 24B is the sample interface 216 that displays diet management technique 218 for recognized health condition with additional scientific and nutritional information about the recommended diet 217.

FIG. 25A and FIG. 25B show a size adjustable clipper embodiment form of the telemetry apparatus. The ring apparatus comprises of a main ring body 219 packaged with electronics and a mechanical gripping element 220. The near-infrared signal probe set 224, transmittive green signal probe set 225, infrared signal probe set 226 and red signal probe set 227 are placed on the upper side of the contact surface 223. The near-infrared red detector probe set 230, green detector probe set 231, infrared detector probe set 232 and red detector probe set 233 are placed in alignment with their corresponding signal probes and on the bottom side of the contact surface 223. The green signal probe 228 and green photodetector probe 229 of the reflective green spectrometer are placed at an optimal adjacent sensing spot of the contact surface 223. A non-contact temperature bio-sensor 234 is embedded on the contact surface for extracting real-time bio-temperature signals and thermal feedback. The contact surface 223 is covered with foam base or sponge 235 around the biosensor, which is used as the mechanical means to enhance the grip and reduce the real-time movement errors. The micro-USB charging and data transfer port 236 and the set of user interaction system of mic 239, micro-speaker 240, buttons 241-242 and navigator crown 243 are embedded on the outer surface 222 of the main ring frame 219. A wireless charging coil 237 and the fancy LED apparatus 238 are embedded inside the ring. The mechanical clipping element 220, comprising of hinge1 244-clip1 245 and hinge2 246-clip2 247, is attached to the main ring frame 219. The clutching action of the hinge1 244-clip 1 245 and hinge2 246-clip2 247 is used as a size adjustable method to grip and fasten the ring apparatus. The entire ring apparatus is further covered with waterproof coating 221.

FIG. 26 shows a dual clipper based ring embodiment form of the telemetry apparatus. The dual clipper comprises of near-infrared and green optical spectrometer apparatus 251 and infrared-red optical spectrometer apparatus 252 embedded on the contact surface 249. The non-sensor area of the contact surface 249 is covered with foam base or sponge like material 253. The button B1 254, button B2 255, button B3 256, navigator crown 257, micro-USB port and other set of user interaction components are embedded on the outer surface 250 of the ring frame 248. The set of upper holding clip 260 and bottom holding clip 261 are attached to the main ring frame 248 through their corresponding movable hinges of 259 and 258. The extender hinge 262 of upper holding clip 260 and extender hinge 263 of bottom holding clip 261 are used as the means to adjust the size of the holding clips. The clutching action of the holding clip 260 with extender hinge 262-holding clip 261 with extender hinge 263 and the movable hinges 259-258 are used as the means to securely fasten the ring device.

FIG. 27A, FIG. 27B and FIG. 27C show the solar powered handheld monitoring embodiment form of the telemetry apparatus.

FIG. 27A shows the front isometric view of the handheld monitoring embodiment form. The micro-USB 265 and a button 266 are embedded on the side surface 264 of the monitor. The mini-touch screen 268 is embedded on the front side of the monitor, which is utilized to operate the apparatus and its inbuilt applications. The monitoring device is covered with waterproof coating 267. The device further comprises of a detachable wearable chord 269. The detachable chord 269 has a chord adjusting element 270 and an extender chord 271 for altering the size of the chord 269.

FIG. 27B shows the back-isometric view of the handheld monitor. A button 273 is embedded on the other side surface 272 of the monitor. A set of a detachable auxiliary powering module comprising of solar module 1 276, solar module 2 277, actuator hinge 275 and actuator 274 are attached to the back surface of the device. The actuator 274 extends the solar module 2 277 through the actuator hinge 275 from plane of solar module 1 276 for harvesting more solar energy. The actuation of the solar module 2 277 occurs automatically or through control command.

FIG. 27C shows the bottom isometric view of the handheld monitor. The bottom surface 278 of the monitor has a finger placement area 279 embedded with bio-sensors in transmittive configuration. The signal probe 281 and detector probe 282 are aligned inside the finger placement area 279 in transmittive sensing configuration. The area around the bio-sensors of the finger placement area 279 is surrounded by foam base or sponge 280. The foam base or sponge 280 is utilized to enhance the grip and reduce the real-time movement errors.

FIG. 28 shows the earphone embodiment of the telemetry apparatus. The earphone apparatus comprises of a transmittive sensing apparatus 283 with an ear placement area 284. The transmittive sensing apparatus 283 is attached to the music ear-bud 287 through the ear hook clip 285. The fancy LED apparatus 286 is embedded inside the earphone near the ear hook 285. The device is covered with water proof coating 288. The ear-bud 287 and the ear hook 285 are used to securely hold the device on the sensing spot. The music ear-bud 287 is further utilized for perceiving audio output.

The above described invention disclosure is intended for illustration purposes, and for those skilled in the art may instantly perceive numerous suggestive modifications, variations and equivalents. Therefore, the disclosure is not exhaustive in broader aspects and the invention is not intended to limit to specific details, spectrometer instruments, illustrated hardware designs, described computational methods and embodiment forms. All equivalents and modifications are intended to be included within the scope of disclosure and attached claims. Accordingly, additional changes and modifications may be made without departing from the scope and the spirit of the invention disclosure appended in the document, claims and their equivalents.

INDUSTRIAL APPLICABILITY

The disclosure presents transmittive sensing based low-powered and totally non-invasive continuous glucose monitoring solution. The described intelligent technology can be utilized as telemetry clinical instrumentation, neo-natal medical device, gestational diabetes monitoring apparatus, real-time diagnostic technology, portable medical apparatus, in-vitro and in-vivo biosensing instrument, general wellness management device, smart wearable device, server based real-time clinical diagnosis system, life-support device, health tracking software device, real-time intelligent medical reminder, automated recommendation system and software medical device.

PRIOR ART AND CITATION LIST

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Hereto the following are claimed:
 1. A multi-spot sensing near-infrared spectrometer apparatus comprising of: a set of three near-infrared light sources arranged on the sensor board with a quantum distance of wavelength number (kλ) between each of the adjacent light source probe; a near-infrared optical lens system arranged at the signal probe end for constructively interfering and focusing the input radiation on the sensing spot; a photodetector end near-infrared optical lens system aligned with the signal probes and assembled at the optimal transmittive response receiving spot, which is utilized for focusing the response signals on the photodetector; and a near-infrared photodetector assembled on a photodetector board at the response receiving spot and aligned with the photodetector end optical lens system.
 2. The apparatus of claim 1 further attached to a multi-spot sensing transmittive green optical spectrometer apparatus, which comprises of: a green LED signal probe arranged on a sensor board; a green optical lens assembled at the signal probe end for focusing and injecting the input green signal on the sensing spot; a green optical lens system aligned with signal probe system at the transmittive response receiving spot for focusing the response signal on the photodetector; and a green photodetector assembled on the sensor board and aligned with the photodetector end optical lens system at the optimal response receiving spot.
 3. The apparatus of claim 2 further attached to a multi-spot sensing tilted reflective green spectrometer apparatus, which comprises of: a green LED signal probe tilted at an angle of critical angle (θc₁) on a sensor mounting board; a green optical lens system tilted at a critical angle (θc₂) and assembled at the signal probe end for focusing and injecting the green signals on the sensing spot at the boundary angle; a photodetector end green optical lens system placed at an optimal distance from the input signal probe for focusing the reflected response on the photodetector system; and a green photodetector assembled on a sensor board at the optimal response receiving spot and aligned with the photodetector end optical lens system.
 4. The apparatus of claim 3 further attached to a multi-spot sensing red indicator spectrometer apparatus, which comprises of: a central red LED signal probe with red optical lens system arranged in the normal direction; two non-central red LED signal probes with their corresponding red optical lens system tilted at an angle to focus the output light on the central photodetection system; a central photodetector-end red optical lens system assembled with its corresponding central red photodetector, which is used as the means to capture and record the non-dispersive red signal response; a non-central photodetector-end red optical lens system with its corresponding non-central red photodetector placed on the left side of the central photodetection system, which is utilized as the means to record the dispersive red signal response; and a non-central photodetector-end red optical lens system with its corresponding non-central red photodetector placed on the right side of the central photodetection system, which is utilized as the means to record the dispersive red signal response.
 5. The apparatus of claim 4 further attached to a multi-spot sensing infrared indicator spectrometer apparatus, which comprises of: a central infrared LED signal probe with infrared optical lens system arranged in the normal direction; two non-central infrared LED signal probes with their corresponding infrared optical lens system tilted at an angle to focus the output light on the central photodetection system; a central photodetector-end infrared optical lens system with its corresponding central infrared photodetector assembled at the central response receiving spot, which is used as the means to record the non-dispersive infrared signal response; a non-central photodetector-end infrared optical lens system with its corresponding non-central infrared photodetector placed on the right side of the central photodetection system, which is utilized as the means to record the dispersive infrared response; and a non-central photodetector-end infrared optical lens system with its corresponding non-central infrared photodetector placed on the left side of the central photodetection system, which is utilized as the means to record the dispersive infrared response.
 6. The optical apparatus of claim 5 further comprising of an inverted arrangement, wherein: the light source probes of near-infrared spectrum, green spectrum, red spectrum and infrared spectrum are placed on the underside of the contact surface; the photodetector probes with their corresponding optical lens system are assembled on the upper side of the transmittive response receiving spot; and the inverted reverse configuration of the signal probes and photodetector probes as the means to curtail the background noise.
 7. The apparatus of claim 6 further comprising of a low-powered telemetry electronic circuit, which comprises of: near-infrared light sources, green LEDs, red LEDs and infrared LEDs with their corresponding optical lens elements, which are arranged according to their respective spectrometer configuration; near-infrared photodetector, green photodetectors, red photodetectors and infrared photodetectors with their corresponding optical elements, which are arranged according to their respective spectrometer configuration; a tuneable BJT/FET based active amplifier attached to the circuit line of near-infrared light probes, which is utilized to coherently drive the light sources and amplify the output; multiple switch set with resistors attached between the corresponding red LED signal probes and infrared LED signal probes, which are utilized to alternatively operate the red and infrared signal probes; the switch set attached between the red LEDs and infrared LEDs as the means to reduce the electronic component use and power consumption; a main switch set attached to the LED frontend and to the input line of signal probes, which is utilized to reduce the frontend components and power consumption; the LED frontend components of LED driver, LED controller, pulse width modulator and clock controller attached to the microprocessor, which are utilized for variably triggering input signal to the signal probes; a multiple switch set attached between the corresponding red photodetector probes and infrared photodetector probes, which are utilized as the means to alternatively extract the red and infrared responses; a darlington pair and a small signal DC source attached to near-infrared response diode, which is used as the means to shift and amplify the output infrared response; a darlington pair and a small signal DC source attached to the transmittive green photodetector probe; a darlington pair and a small signal DC source attached to the tilted reflective green photodetector probe; central and non-central red-infrared photodetection response lines; an op-amp based adder circuit attached to the non-central infrared-red photodetector line, which is utilized to sum the non-central output response; buffer units attached to the central and non-central infrared-red photodetection line for stabilizing the output response; the stabilized central response line and summed non-central response line attached to a circuit line of ADC IC, ambient noise cancellation IC and DAC IC, which is utilized for curtailing the ambient noise in the response line; an instrumental amplifier attached to the filtered central response line and non-central response line, which is utilized for extracting the dispersion information; the red-infrared dispersion analysis circuit line attached to a power notch IC and ADC line connected to the microprocessor, which is used to filter and log dispersion response; a switch set attached to a transimpedance amplifier and to the individual output response lines, which is utilized as a low powered means to extract the individual output responses; the transimpedance amplifier attached to a circuit line of power notch filter, ADC IC and ambient noise cancellation IC, which is utilized to filter the noise in the output response; a 9/6-axis accelerometer, which is utilized as the real-time feedback to remove the movement noise in the real-time recording; the MEMs/NEMs accelerometer as the means for operating the telemetry apparatus through gesture interaction and for computing user and device movement signals; a non-contact bio-temperature sensor for extracting the real-time bio-temperature; the non-contact MEMs/NEMs bio-temperature as the means for recording the real-time thermal feedback of the sensors and electronics; an ambient temperature sensor as the means for extracting the environmental temperature; the ambient temperature sensor as the means for computing the real-time thermal feedback of the electronics; wireless antennae set of GPS antenna, Bluetooth antennae and WLAN antenna, which are used for communicating the data between the telemetry apparatus and the external devices; the wireless antennae set also as the means for computing location and movement related data; a microprocessor attached to a memory component, which is utilized for communicating with the electronics frontend, switches, sensors, wireless antenna and other electronics modules; the microprocessor attached to the memory component as the means for internally executing the code and recording the information; a touch display for viewing and accessing the real-time medical signals, health data and other essential information; the touch display also as the means for operating the instrument and device applications; user interaction components of buttons, navigator crown, mic, and speaker, which are utilized to operate the telemetry apparatus and device applications; the navigator crown, which is made of a potentiometer component and a fixed impedance component; the user interaction components as the means to interact with the professional medical and health practitioners; the user interaction components as the means to perceive the recorded and computed information; a programmable fancy led, which is used to represent different operating modes, device status and decorative applications; the navigator attached to the fancy led as the means for adjusting the intensity of the fancy LED; a power supply unit comprising of power management IC, supercapacitor-battery set, supercapacitor set—energy harvester, wireless charging coil, negative voltage converter and USB module; the power management IC for regulating power supply; the super-capacitor attached to a battery unit, as a more stable means to supply and store energy; the negative voltage converter for generating negative reference signal; the charging coil as the wireless means to power the battery and the internal electronics; the USB module attached to the power management unit and microprocessor, which is utilized to power the telemetry apparatus and recharge the battery; the USB module as the wired means to communicate data with external accessorial devices and server; and the supplementary power supply unit of renewable energy harvester and super capacitor, as the auxiliary means to power the battery and the internal electronics.
 8. The telemetry apparatus of claim 7 further comprising of GSM module, which is utilized as the means to: wirelessly communicate the data; and compute location and movement related data.
 9. The button set of the apparatus of claim 7 further comprising of button B1, button B2 and button B3, wherein: the long hold of button ‘B1’ turns on and off the telemetry apparatus; the short hold of button ‘B1’ swaps the device modes to blood sugar monitoring mode, blood pressure monitoring mode, sleep mode, bio-signal tracking mode, stress management mode, work mode and other in-built modes; the long hold of button ‘B2’ synchronizes the wireless mobile apparatus and transfers data to other synchronized wireless devices; three short holds of button ‘B2’ the turns ON/OFF IOT parallel computation mode; the long hold of button ‘B3’ prompts the user to record the calibration values and real-time biometric values; five short holds button B3 caches and records the subjective emotional stress level mark-ups; the combination long hold of button ‘B1’ and button ‘B2’ silently triggers emergency alert in the wireless life-support network and notifies the life-support network; the combination long hold of button ‘B1’ and button ‘B3’ triggers alarm and medical emergency alert in the wireless life-support network; and the combination programs and individual programs of button ‘B1’, button ‘B2’ and button ‘B3’ are utilized to operate the telemetry apparatus, in-built applications and other device functionalities.
 10. The navigator crown of the apparatus of claim 7, wherein the rotation of the crown: swaps the internal applications in the direction of voltage shift; modifies the intensity of the fancy LED as per the voltage shift; and is utilized to operate the telemetry device and internal applications.
 11. A fancy LED apparatus attached to the telemetry apparatus of claim 7, which comprises of: a main optical tube; a set of optical tubes branching from the main optical tubes and further branching from the branching optical tubes; and a multi-coloured LED embedded inside the main optical tube.
 12. The system and telemetry apparatus of claim 7 further comprising of an accessorial mobile device, which is wirelessly synchronized to the telemetry apparatus, and utilized for: recording the calibration data of blood sugar levels, blood pressure data and other vital biometric information during the instances of device start-up, fasting glucose, pre-breakfast, pre-lunch, pre-dinner, pre-bedtime, post-sleep, post-breakfast, post-lunch, post-exercise and post-dinner; recording user data of username, height, weight, medical condition, fat %, gene info, age, BMI, user picture and other essential information; recording and processing the contact area picture of the user; recording the meal details, meal quantity, macro-nutrient intake and micro-nutrient intake; accessing and viewing the real-time information on detailed diet intake; recording medication alarms and alerts; accessing and viewing the real-time information of blood sugar data, location data, blood pressure signal, neural parameters, pulse rate, oxygen saturation, bio-temperature and other bio-signal information; accessing and viewing the information on daily health, health progress and present health condition; accessing and viewing the real-time data trends on blood sugar levels, blood pressure signal, neural parameters, pulse rate, oxygen saturation, bio-temperature and other bio-signal information; notifying the user with real-time automated medical alerts and medication alarms; prompting the user with automated health recommendation and guidance data; operating the telemetry apparatus; storing and executing the process code, real-time system, analysis methods and software program; storing and processing the computed results; and accessing the in-built applications of the telemetry apparatus.
 13. The real-time telemetry apparatus of claim 7 further comprising of network of computational and storage devices of accessorial wireless mobile devices, server computers and external computers, which are used as the efficient, faster and less complex means to: parallelly and serially execute the real-time system, software programs and computational processes of the telemetry apparatus; and store information, real-time system, process code, user data and computed results.
 14. The telemetry apparatus and the network of devices of claim 13 further comprising of real-time blood sugar monitoring and management system, which comprises of process steps: to calibrate blood sugar values during the instances of device start-up, fasting glucose, pre-breakfast, pre-lunch, pre-dinner, pre-bedtime, post-sleep, post-breakfast, post-lunch, post-exercise and post-dinner; to calibrate the device for hypoglycaemic and hyperglycaemic conditions; to calibrate the device in day interval method for recognized conditions of hypoglycaemia, hyperglycaemia and extreme threshold fluctuations; to calibrate the blood sugar values and other vital information during sitting position, relaxing position, standing position and other postures; to re-record and re-calibrate the blood sugar values for recognized hyperglycaemic conditions, hypoglycaemic conditions and unusual blood sugar fluctuations; to re-record and process the contact skin layer numerous times for eluding calibration errors; to calibrate the contact skin layer based on the normalized index value; to alternatively calibrate the color index based on the realistic profile picture; to record and calibrate the user data of fat %, gene info, BMI, weight, height and age; to initiate green sensors and to measure the response values of transmittive green sensor configuration (GN) and reflective green sensor configuration (GT); to determine the DC parameters of GNDC and GTDC of the reflective green response and transmittive green response; to neglect the reflective green parameter for the non-coarse skin layer; to remove the oscillatory green values from the recorded green sensor response values using fast-fourier analysis and other analysis techniques; to extract the DC green parameter of GNDC and GTDC for the transmittive and reflective configuration; to normalize the green parameter DC values; to deduce the green parameter GPAR value from the processed green sensor response values, GNDC and GTDC; to initiate the different modes of near-infrared sensors; to modify the near-infrared response as per temperature stats of the recorded body temperature and ambient temperature; to adjust the processed Near-Infrared response modes as per resonant power loss characteristics; to compute the normalized near-infrared response (NIRT) from the various recorded and processed infrared modes through numerical statistical methods; to derive oscillatory red response mode Rosc from the recorded total red power response Rtot; to adjust the total red power response (Rtot) and oscillatory red response mode (Rosc) from the green parameter GPAR for the skin losses; to extract blood line losses free 1^(st) order normalized near-infrared sensor value (NIRT1) from the oscillatory red response (Rosc) and normalized near-infrared response (NIRT) using linear and non-linear analysis method; to extract 2^(nd) order near-infrared sensor value (NIRT2) from the 1^(st) order near-infrared response value (NIRT1), and total infrared response (IRtot) and dispersion infrared response values (IRdis) using linear and non-linear analysis, which is used as the means to compensate for the non-haemoglobin particle and other blood particle losses; to derive 3^(rd) order near-infrared response values (NIRT3) from the red dispersion response values (Rdis) and normalized 2^(nd) order value of NIRT2 using linear and non-linear methods; to derive 4^(th) order near-infrared response values (NIRT4) from the green parameter (GPAR) and 3^(rd) order near-infrared response (NIRT3) utilizing the equation form of either power exponent or linear representation of unknown intercept and unknown coefficient; to modify the processed near-infrared response as per the computed contact layer index using non-linear and linear correlation; to correlate the processed near-infrared values and the recorded real-time blood sugar calibration values utilizing non-linear and linear correlation; to calibrate the correlated near-infrared response and the other sensor values for tracking real-time continuous blood sugar values; and to run re-calculation and re-calibration for the recognized conditions of hypoglycaemia, hyperglycaemia and extreme threshold fluctuations; to run learning methods on the recorded infrared and red response for tracking the real-time blood sugar values, hypoglycaemia, hyperglycaemia and extreme threshold fluctuations from the real-time red and infrared response; to store and display continuous blood sugar level (BSL) and blood sugar fluctuation (ΔBSL); to analyse and evaluate fasting glucose BSL value, post-meal BSL value, normal state BSL value, pre-sleep BSL value, post-sleep value and pre-meal BSL value for predicting prediabetic condition, hyperglycaemia condition, and hypoglycaemia condition; to analyse and evaluate fasting glucose BSL variation (ΔBSL1), post-meal BSL variation (ΔBSL2), normal state BSL variation (ΔBSL3), pre-sleep BSL variation (ΔBSL4), post-sleep variation (ΔBSL5) and pre-meal BSL variation (ΔBSL6) for predicting prediabetic threshold fluctuation, hyperglycaemia threshold fluctuation, and hypoglycaemia threshold fluctuation; to analyse and evaluate BSL fluctuation values (ΔBSL) and real-time BSL values with respect to user location for threshold values; to analyse and evaluate the visible and infrared radiation dispersion values [(Rtot-R)/Rtot, R/Rtot, Rdiff/Rtot, IRdiff, IRtot, _ _ _ ] for threshold blood sugar conditions and unusual blood sugar fluctuations of hyperglycaemia, hypoglycaemia and prediabetes; to analyse and evaluate the pulse rate with the blood sugar data for threshold blood sugar conditions and other health issues; to inform the user regarding the recognized health condition, present blood sugar levels and current blood sugar fluctuations; to display and verify probable symptoms of the recognized health condition; to automatically generate recommendations from the database on therapy methods, treatment centres, lifestyle practices, diet suggestions, physical activities, mitigation methods and medication advice to treat and manage the recognized blood sugar condition; to automatically suggest the user with the automated recommendations to recover and improve the diagnosed health condition; to automatically notify the user to re-record and re-calibrate the device for the initial diagnosis of threshold conditions; and to automatically alert and notify the life-support network with a warning message and information on user data, user condition, user location, recognized health condition, present blood sugar levels, current blood sugar fluctuation and other essential data; to automatically inform the user with information on location of the medication; and to automatically remind the user to take medication.
 15. The real-time system and apparatus of claim 14, further comprising of real-time blood pressure monitoring and management system, which comprises of process steps: to calibrate blood pressure values during the instances of device start-up, fasting glucose, pre-breakfast, pre-lunch, pre-dinner, pre-bedtime, post-sleep, post-breakfast, post-lunch, post-exercise, post-meditation and post-dinner; to calibrate the device for hypertension and hypotension conditions; to calibrate the device in day interval method for recognized conditions of hypotension, hypertension and threshold fluctuations; to calibrate the blood pressure values and other vital information during sitting position, relaxing position, standing position and other postures; to re-record and re-calibrate the blood pressure values for hypertension conditions, hypotension conditions, and unusual blood pressure fluctuations; to modify the total red or infrared signal power response (Rtot) from the derived green signal response parameter (GPAR) utilizing the equation form of logarithmic linear representation of unknown intercept and coefficient [Rtot1=Rtot-Y.ln(GPAR)+C1]; to derive red response signal oscillatory mode (RTosc) from the modified red signal response (Rtot1) utilizing fast fourier analysis and other processing techniques; to obtain total power spectrum response of the red signal (RP) from the modified red signal response (RTosc) through non-linear processing of averaged temporal data over peak-peak cycle [RP=Σ∫₀ ^(tpeak)|RTosc|{circumflex over ( )}2]; to correlate the processed red power response and the recorded real-time blood pressure calibration values utilizing non-linear and linear correlation [BP=X.RP]; to calibrate the correlated red signal response and the other sensor values for tracking real-time continuous blood pressure values; to verify the threshold conditions for null movement data; to run re-calculation and re-calibration for the recognized conditions of hypotension, hypertension and threshold blood pressure fluctuations; to store and display the real-time blood pressure levels and blood pressure fluctuations; to analyse and evaluate fasting glucose blood pressure values, post-meal blood pressure values, pre-sleep blood pressure values, post-sleep blood pressure values, normal state blood pressure values, and pre-meal blood pressure values for their corresponding hypertension threshold condition, hypotension threshold condition, and pre-hypertension threshold condition; to analyse and evaluate fasting glucose blood pressure variation, post-meal blood pressure variation, pre-sleep blood pressure variation, post-sleep blood pressure variation, normal state blood pressure variation values and pre-meal blood pressure variation for recognizing prehypertension threshold fluctuation, hypertension threshold fluctuation, and hypotension threshold fluctuation; to analyse and evaluate blood pressure fluctuation and real-time blood pressure with respect to user location for threshold values; to inform the user regarding recognized health condition, present blood pressure level and current blood pressure fluctuation; to display and verify probable symptoms of the recognized health condition; to automatically generate recommendations from database on therapy methods, treatment centres, lifestyle practices, diet suggestions, physical activities, mitigation methods and medication advice to treat and manage the recognized blood pressure condition; to automatically suggest the user with the automated recommendations to recover and improve the diagnosed health condition; to automatically notify the user to re-record and recalibrate the device for the initial diagnosis of threshold conditions; and to automatically alert and notify the life-support network with a warning message and information on user data, user condition, user location, recognized health condition, present blood pressure level, current blood pressure fluctuation and other essential data; to automatically inform the user regarding the location of the medication; and to automatically remind the user to take medication.
 16. The system and the apparatus of claim 15, further comprising of real-time automated emotional stress recognition system, which comprises of process steps: to analyse and evaluate real-time blood pressure values and blood pressure fluctuation for the emotional stress threshold limits; to measure and cache peak to peak temporal red signal response; to measure and cache summed peak to peak temporal red response (HP3); to measure and incrementally sum cache square power peak to peak temporal red signal response (HP2); to evaluate peak to peak temporal fluctuation for 0.05 s intervals and cache a parameter for positive outcomes (HP1); to assess extracted real-time neural parameters with the resting state and active state neural parameters for evaluating the state of emotional stress and anxiety; to analyse and verify the location of the user to assess the psychological burden and the emotional response category; and to inform the user with information on the current stress and emotional condition; to display and verify probable symptoms; to automatically generate recommendations from database on recovery techniques, meditation methods, therapy methods, treatment centres, lifestyle practices, diet suggestions, physical activities, mitigation methods and health advice to manage the recognized emotional state; to automatically suggest the user with automated recommendations to manage the recognized emotional condition; to automatically alert and notify the life-support network with a warning message and information on user data, user condition, user location, recognized health condition and other essential data; to automatically connect the user with social network based on recognized health condition; and to automatically assist the user with information on guided meditation.
 17. The system and apparatus of claim 16, further comprising of real-time automated sleep recognition system, which comprises of process steps: to evaluate and analyse the null movement of the user; to assess the body temperature and bio-signals for realistic range and sleep range; to assess the blood sugar levels and blood pressure data for the state of sleep; to analyse and evaluate the blood sugar levels, blood pressure data and neural parameters (of HP1, HP2 and HP3) with sleep and wake data for recognizing the REM and NREM sleep cycle; to cache the sleep duration, sleep health, REM phase duration and NREM phase duration; to re-evaluate and verify the movement data of actimeter for recognizing sleep health; to assess and verify the state of disturbed sleeps; to store and inform the user with information on the computed sleep data and sleep health; to verify and display probable symptoms; to automatically generate recommendations from database on recovery techniques, meditation methods, therapy methods, treatment centres, lifestyle practices, diet suggestions, physical activities, mitigation methods, medications and health advice to manage the recognized sleep health disorder; to automatically suggest the user with the automated recommendations to manage the diagnosed sleep health disorder; to automatically alert and notify the life-support network with a warning message and information on user data, user condition, user location, recognized health condition and other essential data; and to apply learning on the processing method and real-time system for minimizing analysis parameter count, mode switching, complexity and power consumption.
 18. The real-time system and apparatus of claim 17 further comprising of a real-time alert and recommendation system, which analyses real-time biological information of the user and: automatically notifies the user with real-time alerts for unusual real-time fluctuations of blood sugar data, blood pressure signal, neural parameters, pulse rate, oxygen saturation, bio-temperature and other bio-signal information; automatically notifies the user with medication reminder for the unusual real-time values of blood sugar data, blood pressure signal, neural parameters, pulse rate, oxygen saturation, bio-temperature and other bio-signal information; automatically notifies the user with real-time values and data trends on the blood sugar levels, blood pressure signal, neural parameters, pulse rate, oxygen saturation, bio-temperature and other bio-signal information; automatically notifies the user with health sense message; automatically notifies the user with information on daily health, health management progress, diet records, nutrition intake and present health condition; automatically suggests information on recovery techniques, meditation methods, therapy methods, treatment centres, lifestyle practices, diet plans, physical activities, mitigation methods and health advice to manage the recognized health condition; automatically informs the user with information on effectiveness of the recommendation; and automatically notifies the user with guided health management and therapeutic methods to alleviate the recognized health condition.
 19. The real-time system and apparatus of claim 18 further comprising of a real-time tracking system, which comprises of: an automated interface with real-time information on blood sugar data, user location, blood pressure index, neural parameters, pulse rate, oxygen saturation, bio-temperature and other bio-signal information; an automated interface for recording and accessing meal details, meal quantity, macro-nutrients and micro-nutrients; an automated interface with real-time blood pressure trends; an automated interface with real-time pulse rate and oxygen saturation trends; an automated interface to view real-time blood sugar levels trends; an automated interface with real-time data projection on the neural activity, emotional stress and bio-temperature activity; and an automated interface with health sense information.
 20. The real-time system and apparatus of claim 19 further comprising of a life-support system, which comprises of: life-support network of synchronized support devices, near-by-mobile devices, central server machine, SOS network and devices-in-user vicinity; process step to analyse the real-time physiological and psychological information for recognizing the health condition; process step to automatically trigger emergency based on the recognized health condition; process step to recognize the manual emergency trigger of the user's telemetry apparatus; process step to evaluate the status of the internal wireless antennae set; process step to turn on the switched off wireless antennae; process step to cache user location through the wireless antennae set; process step to cache the real-time physiological and psychological information; process step to communicate the recorded bio-signal data, user's health and medical condition, and location data between the life-support network through wireless methods; process step to communicate the data through the medium of central server and through other shorter robust wireless pathways; process step to recognize the alert trigger command and to notify the auxiliary life-support devices through the primary life-support network; data synchronization process step to detect the information transfer and obtain the communication acknowledgement from the life-support client devices and primary device; data synchronization process step to trigger for sequential dataset communication; and data synchronization process step to receive the proceeding dataset from primary apparatus after the acknowledgement and the sequential dataset trigger steps.
 21. The telemetry apparatus and network of devices of claim 13 and further comprising of a learning method, wherein: the recorded parameters of color index, age, BMI, fat %, gene Info, sensor intensity, signal response, health condition, health data and calibrated medical data of the user are transferred to the central server; the recorded parameters are processed and matched with the user database for learning and deriving the optimization parameters of color index, sensor calibration data, healthy H.R. index, performance index and health progress index; the derived and learnt parameters are returned to the user devices; and the optimization and learnt parameters are utilized by the user device for processing the real-time biological information, health parameters and other essential data.
 22. Single clipper based smart ring embodiment form of the telemetry apparatus of claim 7, comprising of: a main ring frame; near-infrared signal probe set, infrared signal probe set, red signal probe set and green signal probe set with their corresponding optical lens system, which are placed on contact surface of the main ring frame; tilted green signal probe set and reflective green photodetector set with their corresponding optical lens system, which are aligned in reflective configuration and placed on the contact surface; near-infrared photodetector set, infrared photodetector set, red photodetector set and transmittive green photodetector set with their corresponding optical lens system, which are placed on the transmittive response receiving spot of the contact surface; the plurality of photodetector probes aligned with their respective signal probes; the plurality of signal and photodetector probes arranged in the blood flow direction; a MEMs/NEMs bio-temperature sensor embedded on the contact surface; a foam base placed on the contact surface, which is utilized for reducing real-time movement errors and improving multi-use employability; a wireless charging coil embedded inside the main ring frame; a fancy led apparatus placed inside the main ring frame; a micro-USB port placed on the outer surface of the main ring frame; a mic and micro-speaker placed on the outer surface of the main ring frame; a navigator crown placed on the outer surface of the main ring frame; button B1 and button B2 set placed on the outer surface of the main ring frame; water proof coating; a protruding mechanical clipper element with clip 1 and clip 2 attached to the main ring frame; a hinge at the intersection of the main ring frame and mechanical clipper, which is utilized to attach the mechanical clipper to the main ring frame; a hinge at the intersection of clip 1 and clip 2; and the clutching action of the hinges and clips set, as the size adjustable method to fasten the ring and to reduce real-time movement errors.
 23. Dual-clipper based smart ring design of the device of claim 22, which comprises of: a main ring frame; near-infrared optical spectrometer, green optical spectrometer, red optical spectrometer, infrared optical spectrometer and bio-temperature sensor; the plurality of optical spectrometers and bio-sensor embedded on the contact surface of the main ring frame; a navigator crown placed on the outer surface of the main ring frame; button B1, button B2 and button B3 placed on the outer surface of the main ring frame; a foam base placed on the contact surface for reducing real-time motion errors; an upper holding clip attached to the top surface of the main ring frame through a movable clip hinge; the upper holding clip protruding towards the bottom surface of the ring frame; a bottom holding clip attached to the bottom surface of the main ring frame through a movable clip hinge; the bottom holding clip protruding towards the top surface of the ring frame; the clutching action of the holding clips as the means to fasten the device; an extender hinge on the upper holding clip; an extender hinge on the bottom holding clip; and the extender hinges on the holding clips as the means to adjust the clip size.
 24. Ear attachment embodiment form of the telemetry apparatus of claim 7, which comprises of: a transmittive bio-sensing apparatus affixed on the ear-attachment spot; an ear placement area with plurality sensors embedded on the contact surface; an ear-hook and clip attached to the transmittive sensing apparatus; the ear-hook as the means to securely fasten the device; a music ear-bud attached to the earhook, which is utilized as the means for perceiving the device output and for holding the device on the sensing spot; a fancy LED apparatus embedded inside the earhook; and waterproof coating.
 25. Perpetual Handheld Monitoring embodiment form of the telemetry apparatus of claim 7, which comprises of: a finger placement area; signal probe set, photodetector probe set and bio-temperature sensing probe embedded inside the finger placement area in the transmittive configuration; a foam cushioning embedded on the contact surface of the finger placement area around the sensing probes, which is utilized as the means to reduce contact vibration and movement errors; button 1 and button 2 placed on the side surface of the device; micro-USB port placed on the side surface; a touch screen embedded on the front surface, which is used as the means for recording the calibration data, diet information, exercise patterns and other essential information; the touch screen as the means for accessing and viewing the real-time biological information and health data; the touch screen as the means for operating the telemetry apparatus, in-built applications and other essential device functionality; a detachable accessorial renewable charging unit attached to the back surface of the apparatus; a fixed mini solar module and an actuatable mini solar module of the renewable charging unit, which are attached to each other through an actuation hinge; an actuator attached to the actuation hinge, which is utilized as the means to automatically extend the actuatable mini solar module for harvesting more energy; a detachable wearable chord with extender chord and chord adjusting element attached to the main device; and the extender chord and the chord adjusting element as the means to modify the length of the wearable chord; and waterproof coating. 